Customer Segmentation Classification

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library(ggplot2)

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library(tidyverse)

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library(readxl)
library(dplyr)
library(plotly)

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data_col<-read_excel("C:/Users/jangr/Downloads/Customers.xlsx")
View(data_col)

Including Plots

You can also embed plots, for example:

gender_val<- data_col %>% group_by(Gender) %>% summarise(total=n())
# pie- chart
pie_chart<- plot_ly(gender_val,labels= ~Gender , values = ~total,type="pie", textinfo="percent+label", hoverinfo="label+value") %>% layout(title=" Gender analysis")
pie_chart

#bar_chart
total_sales<- data_col %>% group_by(Gender) %>% summarise(total_sales=mean(Monthly_Expenses))
ggplot(total_sales,aes(x=Gender, y=total_sales,fill=Gender))+geom_bar(stat="identity")+
  labs(title="Average Income by Gender", x= "Gender", y="Average Income")+theme_minimal()

ggplot(data_col,aes(x=Age,y=Monthly_Expenses))+geom_point()+
  labs(title="Expenses",x="Age",y="Monthly Expenses")+
  theme_minimal()

mall_val<- data_col %>% group_by(favourite_Mall) %>% summarise(total=n())
# pie- chart
pie_chart<- plot_ly(mall_val,labels= ~favourite_Mall , values = ~total,type="pie", textinfo="percent+label", hoverinfo="label+value") %>% layout(title="Favourite Malls")
pie_chart

ggplot(data_col,aes(x= Gender,y= Spending_Score))+
  geom_boxplot()+
  labs(title = "Spending Score by Gender",x="Gender",y="Spending Score")+
  theme_minimal()

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.